Muth [1]} laid the foundation for exponential smoothing by showing that it provides optimal estimates using random walk with some noise. Further works were done to provide a statistical framework for exponential smoothing leading to the creation of linear models [2]}, [3]}. Unfortunately, this does apply to nonlinear models. Yule postulated a formulation of time series as a realization of a stochastic process [4]}. Exponential Weighted Moving Average (EWMA) is ideal for keeping a set of running moments in the data stream. EWMA is a smoothing technique that adds a forgetting parameter to modify the influence of a recent item in the data stream as shown in Equation REF . This smoothing causes volatility in abrupt transient changes and is unfit for distribution shifts.
\(\mu _{t+1} = \alpha \mu _{t-1} + (1 - \alpha ) X_{t}\) 
